Angular Velocity Measurement With Improved Scale Factor Based on a Wideband-Tunable Optoelectronic Oscillator

Zhang, Jing; Wang, Muguang; Tao, Yu; Ding, Qi; Wang, Chuncan; Huang, Xiaodi; Chen, Desheng; Yan, Fengping

doi:10.1109/tim.2021.3067183

Cited by 30 publications

(13 citation statements)

References 30 publications

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“…It systematically analyzes and studies theoretically the conditions under which the ERM principle is established, the internal relationship between empirical risk and expected risk in the case of limited sample data, and how to find a new learning method according to the above theory. In the final analysis, the core problems to be solved by statistical learning theory mainly include three aspects: first, to solve the conditions of statistical learning consistency under the ERM principle; secondly, how to deal with the relationship between empirical risk and actual risk reasonably and establish appropriate criteria to adapt to the limited data; finally, the algorithm is implemented by using the appropriate basic theory [22][23][24][25].…”

Section: Complexitymentioning

confidence: 99%

[Retracted] Stability Evaluation of Rock Slope in Hydraulic Engineering Based on Improved Support Vector Machine Algorithm

Zhang

2021

Complexity

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The safety problem of the slope has always been an important subject in engineering geology, which has a wide range of application background and practical significance in reality. How to correctly evaluate the stability of the slope and obtain the parameters of the slope has always been the focus of research and production personnel at home and abroad. In recent years, various artificial intelligence calculation methods have been applied to the field of rock engineering and engineering geology, providing some new ideas for the solution of slope stability analysis and parameter back analysis. Support vector machine (SVM) algorithm has unique advantages and generalization in dealing with finite samples and highly complex and nonlinear problems. At present, it has become a research hotspot of intelligent methods and has been widely paid attention to in various application fields of slope engineering. In this paper, a cuckoo search algorithm-improved support vector machine (CS-SVM) method is applied to slope stability analysis and parameter inversion. Aiming at the problem of selecting kernel function parameters and penalty number of SVM, a method of using cuckoo search algorithm to improve support vector machine was proposed, and the global optimization ability of cuckoo search algorithm was used to improve the algorithm. Aiming at the slope samples collected, the classification algorithm of support vector machine (SVM) was used to identify the stable state of the test samples, and the improved SVM algorithm was used to analyze the safety factor of the test samples. The results show that the proposed method is reasonable and reliable. Based on the inversion of the permeability coefficient of the test samples by the improved support vector machine, the comparison between the inversion value and the theoretical value shows that it is basically feasible to invert the permeability coefficient of the dam slope by the improved support vector machine.

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Section: Complexitymentioning

confidence: 99%

[Retracted] Stability Evaluation of Rock Slope in Hydraulic Engineering Based on Improved Support Vector Machine Algorithm

Zhang

2021

Complexity

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“…This has become a significant issue for the Iranian energy market [11][12][13][14][15][16][17]. Traditional fossil fuel-based power plants are not able to provide the power demand [17][18][19][20][21][22][23][24], and blackouts are inevitable, especially in the hot months of the year [25][26][27][28][29][30][31][32][33]. Meanwhile, renewable energy sources (RESs) are one of the efficient solutions for dealing with such an important issue [34][35][36][37][38][39][40][41][42][43].…”

Section: Introduction 1motivationsmentioning

confidence: 99%

Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

et al. 2021

Energies

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The optimal planning of grid-connected microgrids (MGs) has been extensively studied in recent years. While most of the previous studies have used fixed or time-of-use (TOU) prices for the optimal sizing of MGs, this work introduces real-time pricing (RTP) for implementing a demand response (DR) program according to the national grid prices of Iran. In addition to the long-term planning of MG, the day-ahead operation of MG is also analyzed to get a better understanding of the DR program for daily electricity dispatch. For this purpose, four different days corresponding to the four seasons are selected for further analysis. In addition, various impacts of the proposed DR program on the MG planning results, including sizing and best configuration, net present cost (NPC) and cost of energy (COE), and emission generation by the utility grid, are investigated. The optimization results show that the implementation of the DR program has a positive impact on the technical, economic, and environmental aspects of MG. The NPC and COE are reduced by about USD 3700 and USD 0.0025/kWh, respectively. The component size is also reduced, resulting in a reduction in the initial cost. Carbon emissions are also reduced by 185 kg/year.

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“…e collection of geospatial information of landslides is very important and fundamental in the spatial modeling process [12]. In this study, we have used data of 398 landslide events extracted from the Geological Survey of India report (http://www.portal.gsi.gov.in) and by the interpretations of Google Earth images [57]. Landslide polygons delineated were represented by points on the maps for the modeling purpose.…”

Section: Data Usedmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

Trinh

Dam

Al‐Ansari

et al. 2021

Advances in Civil Engineering

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Landslides are one of the most devastating natural hazards causing huge loss of life and damage to properties and infrastructures and adversely affecting the socioeconomy of the country. Landslides occur in hilly and mountainous areas all over the world. Single, ensemble, and hybrid machine learning (ML) models have been used in landslide studies for better landslide susceptibility mapping and risk management. In the present study, we have used three single ML models, namely, linear discriminant analysis (LDA), logistic regression (LR), and radial basis function network (RBFN), for landslide susceptibility mapping at Pithoragarh district, as these models are easy to apply and so far they have not been used for landslide study in this area. The main objective of this study is to evaluate the performance of these single models for correctly identifying landslide susceptible zones for their further application in other areas. For this, ten important landslide affecting factors, namely, slope, aspect, curvature, elevation, land cover, lithology, geomorphology, distance to rivers, distance to roads, and overburden depth based on the local geoenvironmental conditions, were considered for the modeling. Landslide inventory of past 398 landslide events was used in the development of models. The data of past landslide events (locations) was randomly divided into a 70/30 ratio for training (70%) and validation (30%) of the models. Standard statistical measures, namely, accuracy (ACC), specificity (SPF), sensitivity (SST), positive predictive value (PPV), negative predictive value (NPV), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), were used to evaluate the performance of the models. Results indicated that the performance of all the models is very good (AUC > 0.90) and that of the LR model is the best (AUC = 0.926). Therefore, these single ML models can be used for the development of accurate landslide susceptibility maps. Our study demonstrated that the single models which are easy to use and can compete with the complex ensemble/hybrid models can be applied for landslide susceptibility mapping in landslide-prone areas.

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Angular Velocity Measurement With Improved Scale Factor Based on a Wideband-Tunable Optoelectronic Oscillator

Cited by 30 publications

References 30 publications

[Retracted] Stability Evaluation of Rock Slope in Hydraulic Engineering Based on Improved Support Vector Machine Algorithm

[Retracted] Stability Evaluation of Rock Slope in Hydraulic Engineering Based on Improved Support Vector Machine Algorithm

Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

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